Deep learning algorithms are starting to be used to automatically analyze, label, and/or correct abnormalities in images obtained with electron microscopes (EM). Traditionally, these tasks require an expert operator to individually assess the electron microscopy images. This traditional operator-based processes can take huge amounts of time (e.g., multiple hours, weeks, and/or months) to identify defects in electron microscopy images. Deep learning has been shown to drastically speed up this process.
However, before deep learning can be used to analyze, label, and/or correct abnormalities in electron microscopy images, deep learning algorithms must first be trained. To perform this training, training sets of labeled EM images must first be acquired. While EM imaging systems are able to obtain high resolution images of tiny regions of a sample, EM systems are unable to acquire composition information about the sample. Therefore, generating such a training set of labeled EM images requires an expert operator to spend hours to mark each pixel that contains a different material based on a different contrast level in the image. For example, an individual scanning EM image of a semiconductor with a resolution image of 1K×1K pixels can take 4 hours to segment. Accordingly, it is desired to have a more efficient process for generating training sets of labeled EM images